In today’s cybersecurity landscape, time is everything. Threat actors operate at machine speed, and enterprise security teams must match—or beat—that speed to protect their environments. For a global leader like CrowdStrike, enabling real-time analytics isn’t just a technical preference—it’s a strategic imperative.
In a Real-Time Analytics (RTA) Summit 2025 session, a Senior Software Engineer at CrowdStrike shared how the company architected a high-throughput real-time analytics system on Apache Pinot to support mission-critical use cases. Their approach offers valuable lessons for any enterprise dealing with large-scale streaming data, from system load protection to security analytics.
The Need for Real-Time Analytics in Cybersecurity
At the core of CrowdStrike’s mission is staying ahead of adversaries. This demands real-time visibility into massive streams of security telemetry—CPU spikes, suspicious outbound traffic, or anomalous login behavior. These insights must be accessible to automated systems and human analysts to mitigate threats before they escalate.
According to theCUBE Research, over 70% of enterprises now rank real-time data processing as “critical” to their cybersecurity strategies. Yet only 24% believe their current analytics infrastructure can scale to meet that need, highlighting a gap that CrowdStrike’s system is designed to bridge.
Two Strategic Use Cases
1. System Load Protection
CrowdStrike’s microservices architecture includes tens of services listening to a central Kafka topic. These services are responsible for both high-throughput user-facing requests and context-building operations. They run under strict SLAs and SLOs, but the volume of events on Kafka fluctuates based on factors like new customer onboarding or threat actor activity.
CrowdStrike uses Apache Pinot to track event volumes in real time to avoid cascading latency issues from false positives or event surges. A rule-based engine detects load spikes and sends signals downstream, enabling each service to invoke its mitigation plan and preserve its SLA.
This dynamic, real-time orchestration aligns with theCUBE Research’s finding that microservice-driven architectures require “intelligent load shedding” to remain resilient under pressure.
2. Security Analytics
In this use case, Pinot supports data egress detection—a common vector for insider threats or compromised credentials. Security analysts can quickly visualize anomalies by ingesting deduplicated data from multiple sources and presenting it in an interactive UI. Real-time dashboards show top destinations, unusual source IPs, and patterns over time, which helps analysts zero in on malicious exfiltration attempts.
As per theCUBE Research, 61% of CISOs identify lateral movement and data egress as the most challenging threat behaviors to catch in real time. CrowdStrike’s Pinot implementation addresses this concern by enabling high-speed context-building across massive event streams.
Tackling Infrastructure Challenges at Scale
Building this system wasn’t without obstacles. CrowdStrike had to navigate key technical challenges, and their solutions reflect engineering maturity worth studying.
Challenge 1: Complex Protobuf Schemas
CrowdStrike’s events are encoded in Protobuf (Protocol Buffers) with nested types, enumerations, and custom ontology wrappers. These schemas evolved frequently, and every update required a Pinot server restart, slowing agility and increasing maintenance overhead.
A dedicated preprocessor layer was introduced to parse the complex Protobuf events and emit lean JSON events. These were purpose-built to match Pinot table schemas, avoiding restarts and improving deserialization performance.
Abstracting transformation logic into preprocessing layers is a best practice for high-scale streaming systems. It decouples schema evolution from ingestion pipelines, a key pattern in modern data architecture as noted in theCUBE Research’s “Streaming Data Stack 2024” report.
Challenge 2: Dynamic Kafka Infrastructure
With Kafka broker addresses changing without notice, CrowdStrike faced ingestion disruptions that could compromise real-time processing.
They built an automated broker list update system that detects address changes and updates Pinot table configurations in real time.
This mirrors the trend in edge-to-core stream systems—resilience through automation. theCUBE Research identifies Kafka and Pinot pairings as a “leader pattern” in the evolution of cloud-native telemetry pipelines.
Challenge 3: Need for Continuous Query Feeds
Running thousands of similar point-in-time queries on a Pinot table across microservice replicas was inefficient.
CrowdStrike developed a query scheduler that transforms individual queries into continuous data feeds. This not only reduced redundant processing but also enabled context-aware decision-making.
Continuous analytics—also called “query as a stream”—is an emerging architecture trend. It blends the speed of stream processing with the flexibility of ad hoc queries, and we expect more organizations to adopt this approach.
Observability at Scale
CrowdStrike’s observability capabilities operate at scale, reflecting the maturity of its real-time analytics infrastructure. The system handles 120,000 events per second per table replica in production and has ingested over 5 billion events into a single table. At peak, their BCS table supports 25,000 queries per second (QPS), demonstrating both performance and resilience under high-demand conditions.
With more than 10 Pinot clusters deployed across five global environments, CrowdStrike stands out as one of the most advanced and scaled implementations of Apache Pinot in active enterprise use today. These figures place CrowdStrike among the most advanced Pinot operators in production today.
Final Takeaway
CrowdStrike’s real-time analytics architecture exemplifies a modern, scalable, and secure approach to processing high-throughput streaming data. By leveraging Apache Pinot, Kafka, and a thoughtfully layered architecture, the team enables not just threat detection but active, intelligent defense.
This case study underscores a key shift in enterprise analytics: from static dashboards and nightly ETLs to intelligent, real-time systems that help security teams outpace adversaries. As theCUBE Research noted, “streaming is no longer just a data engineering concern—it’s a business imperative.”
